import numpy as np
import skfuzzy as fuzz

if __name__ == "__main__":
	# Create universe of discourse in Python using linspace ()
	_UpperCAmelCase          =						np.linspace(start=0, stop=75, num=75, endpoint=True, retstep=False)

	# Create two fuzzy sets by defining any membership function
	# (trapmf(), gbellmf(), gaussmf(), etc).
	_UpperCAmelCase          =						[0, 25, 50]
	_UpperCAmelCase          =						[25, 50, 75]
	_UpperCAmelCase          =						fuzz.membership.trimf(X, abca)
	_UpperCAmelCase          =						fuzz.membership.trimf(X, abca)

	# Compute the different operations using inbuilt functions.
	_UpperCAmelCase          =						np.ones(75)
	_UpperCAmelCase          =						np.zeros((75,))
	# 1. Union = max(µA(x), µB(x))
	_UpperCAmelCase          =						fuzz.fuzzy_or(X, young, X, middle_aged)[1]
	# 2. Intersection = min(µA(x), µB(x))
	_UpperCAmelCase          =						fuzz.fuzzy_and(X, young, X, middle_aged)[1]
	# 3. Complement (A) = (1- min(µA(x))
	_UpperCAmelCase          =						fuzz.fuzzy_not(young)
	# 4. Difference (A/B) = min(µA(x),(1- µB(x)))
	_UpperCAmelCase          =						fuzz.fuzzy_and(X, young, X, fuzz.fuzzy_not(middle_aged)[1])[1]
	# 5. Algebraic Sum = [µA(x) + µB(x) – (µA(x) * µB(x))]
	_UpperCAmelCase          =						young + middle_aged - (young * middle_aged)
	# 6. Algebraic Product = (µA(x) * µB(x))
	_UpperCAmelCase          =						young * middle_aged
	# 7. Bounded Sum = min[1,(µA(x), µB(x))]
	_UpperCAmelCase          =						fuzz.fuzzy_and(X, one, X, young + middle_aged)[1]
	# 8. Bounded difference = min[0,(µA(x), µB(x))]
	_UpperCAmelCase          =						fuzz.fuzzy_or(X, zero, X, young - middle_aged)[1]

	# max-min composition
	# max-product composition

	# Plot each set A, set B and each operation result using plot() and subplot().
	from matplotlib import pyplot as plt

	plt.figure()

	plt.subplot(4, 3, 1)
	plt.plot(X, young)
	plt.title("""Young""")
	plt.grid(True)

	plt.subplot(4, 3, 2)
	plt.plot(X, middle_aged)
	plt.title("""Middle aged""")
	plt.grid(True)

	plt.subplot(4, 3, 3)
	plt.plot(X, union)
	plt.title("""union""")
	plt.grid(True)

	plt.subplot(4, 3, 4)
	plt.plot(X, intersection)
	plt.title("""intersection""")
	plt.grid(True)

	plt.subplot(4, 3, 5)
	plt.plot(X, complement_a)
	plt.title("""complement_a""")
	plt.grid(True)

	plt.subplot(4, 3, 6)
	plt.plot(X, difference)
	plt.title("""difference a/b""")
	plt.grid(True)

	plt.subplot(4, 3, 7)
	plt.plot(X, alg_sum)
	plt.title("""alg_sum""")
	plt.grid(True)

	plt.subplot(4, 3, 8)
	plt.plot(X, alg_product)
	plt.title("""alg_product""")
	plt.grid(True)

	plt.subplot(4, 3, 9)
	plt.plot(X, bdd_sum)
	plt.title("""bdd_sum""")
	plt.grid(True)

	plt.subplot(4, 3, 10)
	plt.plot(X, bdd_difference)
	plt.title("""bdd_difference""")
	plt.grid(True)

	plt.subplots_adjust(hspace=0.5)
	plt.show()







import random
import unittest

import numpy as np
import torch

from diffusers import (
    DPMSolverMultistepScheduler,
    EulerAncestralDiscreteScheduler,
    EulerDiscreteScheduler,
    LMSDiscreteScheduler,
    OnnxStableDiffusionUpscalePipeline,
    PNDMScheduler,
)
from diffusers.utils import floats_tensor
from diffusers.utils.testing_utils import (
    is_onnx_available,
    load_image,
    nightly,
    require_onnxruntime,
    require_torch_gpu,
)

from ..test_pipelines_onnx_common import OnnxPipelineTesterMixin


if is_onnx_available():
	import onnxruntime as ort





class UpperCAmelCase		(   __A   ,			unittest.TestCase    ):


				'''simple docstring'''






				lowerCamelCase_        =   '''ssube/stable-diffusion-x4-upscaler-onnx'''
				def     lowerCAmelCase_  (		self						,      lowercase=0   ):



						"""simple docstring"""





						A_						:							List[str]      = floats_tensor((1, 3, 1_2_8, 1_2_8)						,      rng=random.Random(lowercase   )   )
						A_						:							Union[str, Any]      = torch.manual_seed(lowercase   )
						A_						:							Tuple      = {
						    'prompt': 'A painting of a squirrel eating a burger',
						    'image': image,
						    'generator': generator,
						    'num_inference_steps': 3,
						    'guidance_scale': 7.5,
						    'output_type': 'numpy',
						}
						return inputs
				def     lowerCAmelCase_  (		self   ):



						"""simple docstring"""





						A_						:							List[Any]      = OnnxStableDiffusionUpscalePipeline.from_pretrained(self.hub_checkpoint						,      provider='CPUExecutionProvider'   )
						pipe.set_progress_bar_config(disable=lowercase   )

						A_						:							int      = self.get_dummy_inputs()
						A_						:							Dict      = pipe(**lowercase   ).images
						A_						:							int      = image[0, -3:, -3:, -1].flatten()

						# started as 128, should now be 512
						assert image.shape == (1, 5_1_2, 5_1_2, 3)
						A_						:							Dict      = np.array(
						    [0.697_4782, 0.6890_2093, 0.7013_5885, 0.758_3618, 0.780_4545, 0.785_4912, 0.7866_7426, 0.7874_3863, 0.7807_0223]   )
						assert np.abs(image_slice - expected_slice   ).max() < 1E-1
				def     lowerCAmelCase_  (		self   ):



						"""simple docstring"""





						A_						:							int      = OnnxStableDiffusionUpscalePipeline.from_pretrained(self.hub_checkpoint						,      provider='CPUExecutionProvider'   )
						A_						:							str      = PNDMScheduler.from_config(pipe.scheduler.config						,      skip_prk_steps=lowercase   )
						pipe.set_progress_bar_config(disable=lowercase   )

						A_						:							List[Any]      = self.get_dummy_inputs()
						A_						:							Union[str, Any]      = pipe(**lowercase   ).images
						A_						:							Tuple      = image[0, -3:, -3:, -1]

						assert image.shape == (1, 5_1_2, 5_1_2, 3)
						A_						:							Dict      = np.array(
						    [0.689_8892, 0.5924_0556, 0.5249_9527, 0.5886_6215, 0.5225_8235, 0.5257_2715, 0.6241_4473, 0.617_4387, 0.621_4964]   )
						assert np.abs(image_slice.flatten() - expected_slice   ).max() < 1E-1
				def     lowerCAmelCase_  (		self   ):



						"""simple docstring"""





						A_						:							Union[str, Any]      = OnnxStableDiffusionUpscalePipeline.from_pretrained(self.hub_checkpoint						,      provider='CPUExecutionProvider'   )
						A_						:							Union[str, Any]      = DPMSolverMultistepScheduler.from_config(pipe.scheduler.config   )
						pipe.set_progress_bar_config(disable=lowercase   )

						A_						:							Union[str, Any]      = self.get_dummy_inputs()
						A_						:							Optional[int]      = pipe(**lowercase   ).images
						A_						:							Any      = image[0, -3:, -3:, -1]

						assert image.shape == (1, 5_1_2, 5_1_2, 3)
						A_						:							int      = np.array(
						    [0.765_9278, 0.7643_7664, 0.7557_9107, 0.769_1116, 0.7766_6986, 0.772_7672, 0.775_8664, 0.781_2226, 0.7694_2515]   )

						assert np.abs(image_slice.flatten() - expected_slice   ).max() < 1E-1
				def     lowerCAmelCase_  (		self   ):



						"""simple docstring"""





						A_						:							Optional[int]      = OnnxStableDiffusionUpscalePipeline.from_pretrained(self.hub_checkpoint						,      provider='CPUExecutionProvider'   )
						A_						:							Tuple      = EulerDiscreteScheduler.from_config(pipe.scheduler.config   )
						pipe.set_progress_bar_config(disable=lowercase   )

						A_						:							Any      = self.get_dummy_inputs()
						A_						:							Union[str, Any]      = pipe(**lowercase   ).images
						A_						:							str      = image[0, -3:, -3:, -1]

						assert image.shape == (1, 5_1_2, 5_1_2, 3)
						A_						:							List[Any]      = np.array(
						    [0.697_4782, 0.6890_2093, 0.7013_5885, 0.758_3618, 0.780_4545, 0.785_4912, 0.7866_7426, 0.7874_3863, 0.7807_0223]   )
						assert np.abs(image_slice.flatten() - expected_slice   ).max() < 1E-1







				def     lowerCAmelCase_  (		self   ):



						"""simple docstring"""





						A_						:							List[str]      = OnnxStableDiffusionUpscalePipeline.from_pretrained(self.hub_checkpoint						,      provider='CPUExecutionProvider'   )
						A_						:							Any      = EulerAncestralDiscreteScheduler.from_config(pipe.scheduler.config   )
						pipe.set_progress_bar_config(disable=lowercase   )

						A_						:							Tuple      = self.get_dummy_inputs()
						A_						:							Optional[Any]      = pipe(**lowercase   ).images
						A_						:							str      = image[0, -3:, -3:, -1]

						assert image.shape == (1, 5_1_2, 5_1_2, 3)
						A_						:							List[str]      = np.array(
						    [0.7742_4496, 0.77_3601, 0.764_5288, 0.776_9598, 0.777_2739, 0.773_8688, 0.7818_7233, 0.7787_9584, 0.76_7043]   )

						assert np.abs(image_slice.flatten() - expected_slice   ).max() < 1E-1





@nightly
@require_onnxruntime
@require_torch_gpu
class UpperCAmelCase		(   unittest.TestCase    ):


				'''simple docstring'''






				@property
				def     lowerCAmelCase_  (		self   ):



						"""simple docstring"""





						return (
						    "CUDAExecutionProvider",
						    {
						        "gpu_mem_limit": "15000000000",  # 15GB
						        "arena_extend_strategy": "kSameAsRequested",
						    },
						)
				@property
				def     lowerCAmelCase_  (		self   ):



						"""simple docstring"""





						A_						:							Optional[int]      = ort.SessionOptions()
						A_						:							Dict      = False
						return options
				def     lowerCAmelCase_  (		self   ):



						"""simple docstring"""





						A_						:							Union[str, Any]      = load_image(
						    'https://huggingface.co/datasets/hf-internal-testing/diffusers-images/resolve/main'
						    '/img2img/sketch-mountains-input.jpg'   )
						A_						:							Optional[int]      = init_image.resize((1_2_8, 1_2_8)   )
						# using the PNDM scheduler by default
						A_						:							Optional[int]      = OnnxStableDiffusionUpscalePipeline.from_pretrained(
						    'ssube/stable-diffusion-x4-upscaler-onnx'						,      provider=self.gpu_provider						,      sess_options=self.gpu_options						,      )
						pipe.set_progress_bar_config(disable=lowercase   )

						A_						:							str      = 'A fantasy landscape, trending on artstation'

						A_						:							int      = torch.manual_seed(0   )
						A_						:							int      = pipe(
						    prompt=lowercase						,      image=lowercase						,      guidance_scale=7.5						,      num_inference_steps=1_0						,      generator=lowercase						,      output_type='np'						,      )
						A_						:							Optional[Any]      = output.images
						A_						:							Union[str, Any]      = images[0, 2_5_5:2_5_8, 3_8_3:3_8_6, -1]

						assert images.shape == (1, 5_1_2, 5_1_2, 3)
						A_						:							Tuple      = np.array([0.4883, 0.4947, 0.4980, 0.4975, 0.4982, 0.4980, 0.5000, 0.5006, 0.4972]   )
						# TODO: lower the tolerance after finding the cause of onnxruntime reproducibility issues

						assert np.abs(image_slice.flatten() - expected_slice   ).max() < 2E-2







				def     lowerCAmelCase_  (		self   ):



						"""simple docstring"""





						A_						:							Any      = load_image(
						    'https://huggingface.co/datasets/hf-internal-testing/diffusers-images/resolve/main'
						    '/img2img/sketch-mountains-input.jpg'   )
						A_						:							Optional[int]      = init_image.resize((1_2_8, 1_2_8)   )
						A_						:							int      = LMSDiscreteScheduler.from_pretrained(
						    'ssube/stable-diffusion-x4-upscaler-onnx'						,      subfolder='scheduler'   )
						A_						:							int      = OnnxStableDiffusionUpscalePipeline.from_pretrained(
						    'ssube/stable-diffusion-x4-upscaler-onnx'						,      scheduler=lowercase						,      provider=self.gpu_provider						,      sess_options=self.gpu_options						,      )
						pipe.set_progress_bar_config(disable=lowercase   )

						A_						:							int      = 'A fantasy landscape, trending on artstation'

						A_						:							Tuple      = torch.manual_seed(0   )
						A_						:							Optional[Any]      = pipe(
						    prompt=lowercase						,      image=lowercase						,      guidance_scale=7.5						,      num_inference_steps=2_0						,      generator=lowercase						,      output_type='np'						,      )
						A_						:							List[Any]      = output.images
						A_						:							int      = images[0, 2_5_5:2_5_8, 3_8_3:3_8_6, -1]

						assert images.shape == (1, 5_1_2, 5_1_2, 3)
						A_						:							Tuple      = np.array(
						    [0.5017_3753, 0.5022_3356, 0.50_2039, 0.5023_3036, 0.502_3725, 0.502_2601, 0.501_8758, 0.5023_4085, 0.5024_1566]   )
						# TODO: lower the tolerance after finding the cause of onnxruntime reproducibility issues

						assert np.abs(image_slice.flatten() - expected_slice   ).max() < 2E-2






